data-flow-analysis-framework

Design and implement data-flow analyses for compiler optimization

509 stars

Best use case

data-flow-analysis-framework is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Design and implement data-flow analyses for compiler optimization

Teams using data-flow-analysis-framework should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/data-flow-analysis-framework/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/computer-science/skills/data-flow-analysis-framework/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/data-flow-analysis-framework/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How data-flow-analysis-framework Compares

Feature / Agent	data-flow-analysis-framework	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Design and implement data-flow analyses for compiler optimization

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Data Flow Analysis Framework

## Purpose

Provides expert guidance on designing and implementing data-flow analyses for compiler optimization and program analysis.

## Capabilities

- Forward/backward analysis specification
- Lattice definition and verification
- Transfer function generation
- Fixpoint computation (worklist algorithm)
- Analysis soundness verification
- Interprocedural analysis

## Usage Guidelines

1. **Lattice Design**: Define abstract domain and lattice
2. **Transfer Functions**: Define transfer functions for statements
3. **Analysis Direction**: Specify forward or backward
4. **Fixpoint**: Implement worklist algorithm
5. **Verification**: Verify soundness of analysis

## Tools/Libraries

- LLVM
- GCC internals
- Soot
- WALA

Related Skills

structured-data

509

from a5c-ai/babysitter

JSON-LD schema markup and validation.

user-flow-diagram

509

from a5c-ai/babysitter

Generate user flow diagrams and flowcharts using Mermaid and other formats

heatmap-analysis

509

from a5c-ai/babysitter

Analyze user interaction heatmaps for attention patterns and click behavior

static-analysis-runner

509

from a5c-ai/babysitter

Run static analysis tools including SonarQube, ESLint, and multi-language linters

Static Analysis Tools Skill

509

from a5c-ai/babysitter

Integration with security-focused static analysis tools

Smart Contract Analysis Skill

509

from a5c-ai/babysitter

Ethereum and blockchain smart contract security analysis

Network Protocol Analysis Skill

509

from a5c-ai/babysitter

Network protocol capture, analysis, and fuzzing capabilities

CVE/CWE Database Skill

509

from a5c-ai/babysitter

CVE and CWE database querying and management

oauth-flow-implementer

509

from a5c-ai/babysitter

Implement OAuth 2.0 and OpenID Connect flows for SDKs

contract-test-framework

509

from a5c-ai/babysitter

Consumer-driven contract testing for SDK-API compatibility. Generate Pact consumer tests, verify provider contracts, configure Pact broker, and implement can-i-deploy checks.

cli-framework-builder

509

from a5c-ai/babysitter

Build command-line interfaces for SDK interaction

test-data-generation

509

from a5c-ai/babysitter

Synthetic test data generation and management using Faker.js and similar tools. Generate realistic test data, create data factories, implement database seeding, and manage test data anonymization.